학술논문
Deep Learning for Fully Automatic Tumor Segmentation on Serially Acquired Dynamic Contrast-Enhanced MRI Images of Triple-Negative Breast Cancer.
Document Type
Article
Author
Xu, Zhan; Rauch, David E.; Mohamed, Rania M.; Pashapoor, Sanaz; Zhou, Zijian; Panthi, Bikash; Son, Jong Bum; Hwang, Ken-Pin; Musall, Benjamin C.; Adrada, Beatriz E.; Candelaria, Rosalind P.; Leung, Jessica W. T.; Le-Petross, Huong T. C.; Lane, Deanna L.; Perez, Frances; White, Jason; Clayborn, Alyson; Reed, Brandy; Chen, Huiqin; Sun, Jia
Source
Subject
*DEEP learning
*DIGITAL image processing
*MATHEMATICAL models
*MAGNETIC resonance imaging
*CANCER patients
*TUMOR classification
*THEORY
*RESEARCH funding
*SENSITIVITY & specificity (Statistics)
*BREAST tumors
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Language
ISSN
2072-6694
Abstract
Simple Summary: Quantitative image analysis of cancers requires accurate tumor segmentation that is often performed manually. In this study, we developed a deep learning model with a self-configurable nnU-Net for fully automated tumor segmentation on serially acquired dynamic contrast-enhanced MRI images of triple-negative breast cancer. In an independent testing dataset, our nnU-Net-based deep learning model performed automated tumor segmentation with a Dice similarity coefficient of 93% and a sensitivity of 96%. Accurate tumor segmentation is required for quantitative image analyses, which are increasingly used for evaluation of tumors. We developed a fully automated and high-performance segmentation model of triple-negative breast cancer using a self-configurable deep learning framework and a large set of dynamic contrast-enhanced MRI images acquired serially over the patients' treatment course. Among all models, the top-performing one that was trained with the images across different time points of a treatment course yielded a Dice similarity coefficient of 93% and a sensitivity of 96% on baseline images. The top-performing model also produced accurate tumor size measurements, which is valuable for practical clinical applications. [ABSTRACT FROM AUTHOR]